Integration of Legacy Appliances into Home Energy Management Systems

The progressive installation of renewable energy sources requires the coordination of energy consuming devices. At consumer level, this coordination can be done by a home energy management system (HEMS). Interoperability issues need to be solved among smart appliances as well as between smart and non-smart, i.e., legacy devices. We expect current standardization efforts to soon provide technologies to design smart appliances in order to cope with the current interoperability issues. Nevertheless, common electrical devices affect energy consumption significantly and therefore deserve consideration within energy management applications. This paper discusses the integration of smart and legacy devices into a generic system architecture and, subsequently, elaborates the requirements and components which are necessary to realize such an architecture including an application of load detection for the identification of running loads and their integration into existing HEM systems. We assess the feasibility of such an approach with a case study based on a measurement campaign on real households. We show how the information of detected appliances can be extracted in order to create device profiles allowing for their integration and management within a HEMS

GREEND: An Energy Consumption Dataset of Households in Italy and Austria

Home energy management systems can be used to monitor and optimize consumption and local production from renewable energy. To assess solutions before their deployment, researchers and designers of those systems demand for energy consumption datasets. In this paper, we present the GREEND dataset, containing detailed power usage information obtained through a measurement campaign in households in Austria and Italy. We provide a description of consumption scenarios and discuss design choices for the sensing infrastructure. Finally, we benchmark the dataset with state-of-the-art techniques in load disaggregation, occupancy detection and appliance usage mining

Smart Microgrids: Optimizing Local Resources toward Increased Efficiency and a More Sustainable Growth

Smart microgrids are a possibility to reduce complexity by performing local optimization of power production, consumption and storage. We do not envision smart microgrids to be island solutions but rather to be integrated into a larger network of microgrids that form the future energy grid. Operating and controlling a smart microgrid involves optimization for using locally generated energy and to provide feedback to the user when and how to use devices. This chapter shows how these issues can be addressed starting with measuring and modeling energy consumption patterns by collecting an energy consumption dataset at device level. The open dataset allows to extract typical usage patterns and subsequently to model test scenarios for energy management algorithms. Section 3 discusses means for analyzing measured data and for providing detailed feedback about energy consumption to increase customers’ energy awareness. Section 4 shows how renewable energy sources can be integrated in a smart microgrid and how energy production can be accurately predicted. Section 5 introduces a self-organizing local energy system that autonomously coordinates production and consumption via an agent-based energy auction system. The final section discusses how the proposed methods contribute to sustainable growth and gives an outlook to future research

Assisting energy management in smart buildings and microgrids

Die wachsende Nutzung von erneuerbaren Energien stellt das Stromnetz vor neue Herausforderungen die geeignete MaÃnahmen fÃr die stabile Energieversorgung im Stromnetz erfordern. Der massive Einsatz von SpeichermÃglichkeiten ist hierbei eine vergleichsweise teure und ineffiziente LÃsung. Mechanismen zur Nachfragesteuerung kÃnnen das Ungleichgewicht zwischen Energieangebot und -nachfrage verbessern. Dies inkludiert sowohl direkte als auch indirekte Regelmechanismen, die abhÃ$ngig von der Steuerbarkeit der Lasten eingesetzt werden kÃnnen. In letzterem Fall werden NetzengpÃ$sse und ÉberschÃsse durch einen verÃ$nderlichen Strompreis behandelt. Das erfordert den rechtzeitigen Informationsaustausch zwischen Anbietern und Verbrauchern, konkret Daten zu LeistungsflÃssen und Phasenlage als Grundlage zur Preisbildung. Dazu stehen den Energieversorgern durch den Einsatz von intelligenten StromzÃ$hlern zunehmend genauere Strommessdaten zur VerfÃgung. Solche Daten sind auch fÃr das GebÃ$udemanagement und fÃr Endkunden von Interesse, um den tÃ$glichen Energieverbrauch zu erfassen und auf dieser Grundlage Effizienzverbesserungen zu planen. Diese Arbeit befasst sich mit verschiedene Anwendungen von hochauflÃsenden Stromverbrauchsdaten fÃr das Energiemanagement in Smart Microgrids. Zu diesem Zweck wurde in der ersten Phase eine Messkampagne in ausgewÃ$hlte Haushalten in Italien und Ãsterreich durchgefÃhrt. Der daraus entstehende Datensatz, kurz GREEND, enthÃ$lt mehr als 1 Jahr an detaillierten Verbrauchsdaten die im Sekundenabstand gemessen wurden. Der GREEND-Datensatz wurde verÃffentlicht und fÃr die Forschungsgemeinschaft freigegeben. Er wurde ebenfalls durchgehend in dieser Dissertation verwendet. Diese Arbeit stellt auÃerdem eine Dateninfrastruktur vor, welche das ZusammenfÃhren von Daten aus unterschiedlichen Quellen in dynamischen Umgebungen erlaubt. Im Besonderen werden Architekturanforderungen identifiziert welche die InteroperabilitÃ$t auf GerÃ$te- und Datenebene ermÃglicht. Die vorgestellte LÃsung bietet eine einheitliche Schnittstelle um DatenÃ$nderungen zu verfolgen, was die Umsetzung in konkrete Anwendungen erleichtert. ZusÃ$tzlich wird ein Ontologiemodell vorgestellt welche statische und dynamische Information zu HaushaltsgerÃ$ten modellieren kann. Dies ermÃglicht die volle Integration von intelligenten und konventionellen GerÃ$ten, so dass das Verhalten eines GerÃ$ts allgemein in einer Art elektronisches Datenblatt Ãber das Netz kommuniziert werden kann. Die VerfÃgbarkeit von Energieverbrauchsdaten ermÃglicht das Bereitstellen von Mehrwertdiensten fÃr Endverbraucher und Versorger. Dazu wird die MÃglichkeit eines interaktiven Systems untersucht, welches Verbraucher Ãber ihren Energieverbrauch zeitnah informieren kann um eine effiziente Nutzung von vorhandenen Ressourcen zu fÃrdern. Konkret werden hier basierend auf dem Verbrauchsverhalten und der Wohnungsbelegung VorschlÃ$ge an den Kunden zurÃckgegeben. Unter Verwendung von GREEND konnte fÃr diesen Ansatz ein Einsparungspotential von bis zu 34% ermittelt werden. Die EffektivitÃ$t von Nachfragesteuerungsprogrammen hÃ$ngt jedoch stark von automatisiert steuerbaren GerÃ$ten ab. Zur Umsetzung dieser Vision stellt diese Arbeit im letzten Teil eine HEMS-Marktsimulation vor, welche das automatische Trainieren von GerÃ$testeuerungen ermÃglicht. Da dieser Ansatz auf einem kompetitiven Ansatz beruht liefert er nur bedingt kooperative LÃsung zur GerÃ$testeuerung. Dementsprechend erfolgt die Optimierung nur jeweils fÃr einen bestimmten Zeitraum was potentiell zu Betriebsunterbrechungen fÃhren kann. Um dieses Problem zu lÃsen, wird ein Microgrid Power Broker vorgeschlagen, welcher als Vertriebsstelle fÃr die verfÃgbare Energie agiert. Der Broker versucht dabei den Preis fÃr eine gegebene Leistung Ãber verschiedene Betriebszeiten vorherzusagen und dementsprechend zu handeln. Die drei vorgestellten AnsÃ$tze sind unabhÃ$ngig voneinander einsetzbar und liefern verschiedene BeitrÃ$ge zum Stand der Forschung. Die Ergebnisse bieten eine Basis fÃr zukÃnftige Forschung im Bereich Energiemanagement und in der intelligenten GebÃ$udetechnik.The increasing exploitation of renewable energy sources for power generation introduces a significant instability into the power grid, which has to be addressed with appropriate management strategies. Energy storage is a costly and inefficient solution. Demand-side control mechanisms can help mitigating the unbalance between available supply and demand. This includes both direct and indirect control, depending on the degree of controllability of demand-side loads. In the latter, congestion on the shared resource is managed using a price signal, exchanged throughout the power grid and reflecting the resource availability. This requires the timely exchange of information between energy consumers and producers, namely power and phase measurements to be used for the resource pricing. Furthermore, more fine-grained usage data is progressively becoming available to utilities thanks to the deployment of smart meters. Such an information is also relevant to facility managers and users, to become aware of the energy footprint of daily activities and seek a more efficient usage process. This thesis deals with different applications of high-resolution power usage data for energy management in smart microgrids. To this end, the first stage included a measurement campaign in selected households in Italy and Austria. The resulting dataset, named GREEND, contains more than 1 year consumption data at 1 Hz. GREEND was released to the research community for open use, as well as used throughout the thesis. We elaborate on the design of a data infrastructure capable of collecting data from heterogeneous data sources in highly dynamic environments. Specifically, architectural requirements are identified to achieve interoperability at the level of electrical devices as well as exchanged data. The proposed solution offers a single interface to query for status changes, which eases the application development process. In addition, we propose an ontology modeling both static and dynamic information of household appliances. This allows for the full integration of smart and non-smart devices, whose behavior can be tracked and recorded in a sort of datasheet to be exchanged across the network. The availability of energy usage data allows for the provisioning of value-added services to both end-users and utilities. To this end, we investigate on the possibility of an interactive system to timely inform users on their energy usage, in order to promote an efficient use of local resources. In particular, advices are returned to consumers based on their usage behavior and building occupance. Using the GREEND, this solution alone was quantified as potentially yielding up to 34% of savings. However, the effectiveness of demand response programs is greatly affected by the possibility to automate specific devices. Towards this vision, we introduced the HEMS market simulator, which allows for training appliance controllers. Because of the strictly competitive setting, pure market mechanisms do not offer a complete solution for automatic load management. Accordingly, competition is limited to a specific trading day, and has the potential effect of yielding service interruption. To solve this issue, we propose a microgrid power broker that acts as a retailer of available supply. The broker seeks profit by forecasting the price of different power provisioning durations. The three different approaches are independent and give an individual contribution to the research community. The results provide the basis for future research in the field of energy management systems for microgrids and smart buildings.Andrea MonacchiZusammenfassung in deutscher und in italienischer SpracheAlpen Adria Universität Klagenfurt, Dissertation, 2016OeBB(VLID)241232